sAIfer Lab works to get an in-depth understanding of the cyber threats and to develop plan prevention, detection, and response actions.

Understanding the vulnerabilities that allowed a threat actor to succeed in attacking a system requires collecting and correlating data from multiple sources such as logs, program traces, etc.

Advanced tools for analyzing binary codes, system memory, files, and execution traces have been developed to get an in-depth understanding of the threats and plan prevention, detection, and response actions. Machine- and deep-learning approaches help to extract the most relevant knowledge, while LLMs allow fusing structured and unstructured knowledge.